Embedded Analytics

The Data Monetization Maturity Model

The Evolution of Data Monetization

Watch this video to find out where most organizations are right now in terms of data monetization maturity -- and how to move past that.

A five-stage model describes the path to data monetization maturity. But did you know that although many organizations have invested heavily in data and analytics since the 1990s, most have not moved beyond the first stage of the model -- distributing analytics internally? The next four stages chart the development of analytics from a cost center to a profit center. And each stage has its own requirements.

Transcript

Hi, everyone. In my last video I talked about how data can be as valuable as a product and in some cases more valuable. In this video I want to talk about how you can take your data and analytics assets and start to monetize them. To do that I’m gonna show you a five step maturity model, data monetization maturity model, if you will.

Distribute Analytics Internally

So, let’s begin. So, the first stage is to distribute analytics internally. And we do that by creating the data warehouse, by BI and analytics tools, building the ports and dashboards and giving self-service access to some users. And that has a lot of upshot and benefit; it improves decision-making, optimizes performance and streamlines processes. But, it’s hard to quantify the value of that and certainly there’s no direct revenues that come from that.

Distribute Analytics Externally

So, the next step is also something companies have been doing for many years, which is to distribute analytics externally, usually by sending them, via email, a PDF of a static report. And typically companies have been doing this on a monthly basis, say a telecommunications company, sending a report about how their customers have been using their services and how much they’ve been billed, credit card companies. We all get monthly reports from our retirement funds. Probably the more innovative was Wal-Mart, back 20 years ago, began to provide its suppliers inventory data so they could proactively stock their product on Walmart shelves and that was kind of the state of the art for distributing analytics externally.

Embed Analytics in Applications

And the next stage really is to embed analytics into applications. And this has two very important meanings. One, is it provides context to internal workers working with ERP and CRM applications. So, they don’t have to go to a separate application to do analysis and insight. All the charts, all the reports, all the data is embedded and integrated within their core operational application, which makes them more efficient.

But, again, we’re not really generating revenue here. That comes when we start to embed analytics, both reports, static and interactive, as well as self-service tools inside our web based applications that gives customers the ability to interact with data and begin to not just see a static view of their usage or activity with this supplier, but to slice and dice the data and get more accurate information about how they can optimize what goods and services they’ve purchased from this vendor.

Mining Data for Value

So, let’s look into step four, this is when we actually take the results of all this customer interaction and all the customer activity and how they’re using our products and start to mine it, mine that data historically and create analytical models of that data. We can in turn take that information and give it back to customers in the form of value added information, which makes our products much more valuable and interesting and locks customers into the client.

So, for instance, we can mine our information and start to provide customers benchmark data. So, how does their usage of this product or service compare with other companies who are customers of this client that are the same size and exhibit the same characteristics? Another thing is the ability to provide personalized recommendations based on historical information we think to optimize your use of our product or service, you should be doing these sorts of things. And then we can instrument alerts along those lines as well so that we can automatically notify people if their behavior or usage falls above or below a certain threshold.

Attach a Price Tag to Data Value

Now, in the final stage we actually take data and we take analytics and we start to really monetize and attach a price tag to it. In some cases we just add reporting. We add self-service features to an existing online application, in which case we sweeten the value of that application, make it more attractive for customers to buy it or we can increase the price of that application because we’ve added more features and functionality in the form of data analytics or we can actually sell this as an add-on feature that they buy in addition to the existing product or, in some cases, sell it as a separate product.

I’ve also known companies that have taken their expertise and data and analytics and turned themselves into a service bureau or a consultancy or a data syndication provider that takes aggregated information and sells it back to companies in their industry.

So, that is the five-stage maturity model for data monetization. I hope to see you in one of these later stages sometime soon.

The Data Monetization Maturity Model

Many organizations have invested in data & analytics for decades, but many have not moved beyond internal distribution. Learn how you can.

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